Curriculum vitae.

Dates: • 1991 − 1996: she attended “Liceo Scientifico Statale Paolo Frisi di Monza (Milano)”, where she graduated with 52/60. • October 2001: she graduated as a computer scientist from Università degli Studi di Milano, Department of Computer Science, with grade 110/110 cum Laude. • January 2001: she was hired as a “trainee researcher” by the Multiple Media Department of the VTT Information Technology (Helsinki, Finland). • January 2002: she won a scolarship to start her PhD studies at Università degli Studi di Milano, Department of Computer Science. • September-December 2003: she was a visiting researcher at the “Image Science Institute” (ISI), in Utrecht (The Netherlands). • January 2004: she was hired as a temporary researcher at Università degli Studi di Milano, Department of Computer Science. • March 2005 : she got the Phd degree at Università degli Studi di Milano, Department of Computer Science. • May 2006-September 2009: she was hired as an assistant professor (researcher) at Università degli Studi di Milano, Department of Computer Science “Giovanni degli Antoni”; her permanent position was confirmed on September 2009. • Present: she is an assistant professor (researcher) at Università degli Studi di Milano, Department of Computer Science “Giovanni degli Antoni”.


Fundings and cooperations with industries
• 08 December 2006 -31 December 2007: She worked in cooperation with VIDIEMME CONSULTING S.r.l. to develop an automatic software for the identification of lung nodules at early stages from Postero Anterior chest radiographs [24]. The aforementioned research activity was funded by a contract with VIDIEMME CONSULTING S.r.l..
• 09 April 2009-08 April 2010: She actively worked and supervised the work of researchers in the ex-LAIV Laboratory (Università degli Studi di Milano, Department of Computer Science) during a cooperation with BioDigitalValley S.r.l.. The aim of the one-year contract was the development of novel techniques for medical image processing, and two main projects were delivered. The first one is a software system for the automatic segmentation of anatomical parts of interest from digital mouse images [30]. The second one is a software system for the automatic analysis and alignment of Gel2D images [43]. The aforementioned projects were funded by a contract with BioDigitalValley S.r.l.. • 01 December 2011-31 November 2012: She got funding from "Bando Dote Ricerca Applicata" and she was the Scientific Referee during a one-year cooperation with MOX Consulting SRL. During this year she supervised a researcher developing an application for the industrail control during preparation of spray cans.
• Years 2016-2017-2018: She got funding from "Bando sostegno alla Ricerca -Linea A -Young researchers" to carry out three projects entitled: -2016 Development of an automatic system for blood and lymph vessels localization and quantification from microscopic images of carotid artery sections [12].
-2017 Development of an automatic system for localization and quantification of biological structures of interest from immunohistochemical images [11].
These projects were carried on in cooperation with Consorzio M.I.A. (Università degli Studi di Milano-Bicocca,http://www.consorziomia.org/) and the Department of Experimental Oncology and Molecular Medicine (Fondazione IRCCS Istituto Nazionale dei Tumori).
• 2019: she works in the research group that got the fundings from "Bando sostegno alla Ricerca -Linea B". The project is entitled: "Graph-based Modelling and Optimisation".
• 2020: she works in the research group that got the fundings from "Bando sostegno alla Ricerca -Linea B". The project is entitled: "Human aspects for video, images, and graphics in multimedia systems".
European Projects, AIRC Fundings, Projects with International Groups

Activity as Guest editor and Reviewer
She is Guest Editor of the Special Session "Simulation, Imaging and Modelling for Biomedical Systems", which is part of Computers Journal (ISSN 2073-431X, https://www.mdpi.com/journal/computers).

Activity as a member of Program Committees of Conferences and Workshops
-She is member of the Technical Program Committee of the 25th International Conference on Pattern Recognition (ICPR2020).

Research Activities
Elena Casiraghi's interest in the information technology research field date to the year 2000, when she was hired as a "trainee researcher" in VTT (Valtion Teknillinen Tutkimuskeskus), Department of Information Technology.
During the year spent at VTT, she started to work in the European project "Internet Middleware for Customized Service Bundling" to develop mathematical methods for the automatic insertion of digital photographs into a 3D virtual world; this problem required a continuous adaptation of the image according to the user movements into the 3D world. Her work was highly judged and treated as an important issue during the international reporting of the project. After the end of the project, she worked in a research project studying computational methods for observing early signs of deterioration of CD-ROM discs; after presenting a detailed analysis of the problem, the developed recovering system was considered as efficient and effective.
Since she began to work in Università degli Studi di Milano, Elena Casiraghis research interests have been mainly focused in the field of artificial intelligence, to develop automatic systems for image processing and pattern recognition.
Specifically, she began her researches with investigations in the field of image processing, to develop automatic applications for face localization, identification, and recognition. These problems allowed her to study and apply supervised and unsupervised learning algorithms.
Subsequently, she focused on the medical and biomedical image processing fields, where she studied and developed computer aided diagnosis (CAD) systems [44,42,43,46,44,44,41,25,24,52,61,60,38,37,36,35,23,33,22,20]. In detail, she started working on digital chest radiographs, to detect subtle lung nodules at their early stages; the developed CAD system can be successfully applied to aid radiologists during their decision making process, thus increasing their nodule identification performance. After these researches she focused on the problem of living donor liver transplantation and developed an automatic system for the 3D reconstructions of abdominal organs (e.g. liver, spleen, and kidney) from computed tomography (CT) images, with the final aim of measuring their volume. Both these systems required the development of applications being able to cope with data of high dimensionality. Furthermore, her investigations lead her to the development of learning systems treating highly unbalanced learning datasets of high cardinality.
Other minor researches in the medical field were aimed at 3D volume reconstruction and biometric analysis of fetal brain from MR images.
In the bio-medical field, she developed an automatic system for the segmentation of mice images produced with molecular imaging. The system identifies anatomical organs of interest where it computes specific measurements; the precision of the obtained measures has been considered particularly helpful by pharmacologists that needed to evaluate and compare the pharmacological effect produced by different drugs, that is drugs produced according to different biochemical interactions.
All the aforementioned researches have been performed in cooperation with experts of the "Policlinico e Regina Margherita (Fondazione IRCCS)" hospital of Milan. At the present, she still keeps researching with them; specifically, exploiting the automatic systems she developed, she processes radiological images, extracts relevant data, and performs statistical analises of the extracted data to answer clinical inquiries of medical experts [38,37,36,35,23,33,22,20,7].
In the year 2010-2014, she has been investigating in the field of pattern recognition, manifold learning, and intrinsic dimensionality estimation, to develop novel theories and automatic algorithms dealing with high-dimensional datasets characterized by a small cardinality (Small Sample Size Problem). These researches led to the development of methods whose performance has been evaluated both by the comparison with state of the art techniques and by tests on synthetic and real datasets related to problems in the fields of signal processing, image analysis, and bioinformatics [26,25,24,23]. The aforementioned studies are currently exploited to investigate and experiment solutions to reduce one of the main problem of deep learning techniques, which is the huge computational (time and memory) costs. To this aim, researches are aimed at compressing deep neural networks, by reducing their layer size to the intrinsic dimension estimated on the layers' filters. To effectively reduce the filter dimension different techniques are going to be experimented [21,20].
In the latest years, she has been collaborating with the biological researchers of Consorzio M.I.A -Microscopic Image Analysis (University of Milan-Bicocca) to develop automatic systems for the microscopic image analysis. She started her collaboration with expert biologists, as well as cardiovascular surgeons, to investigate the main factors behind carotid plaques' instability, the latest being the main cause of cerebral stroke. More specifically, during the study she developed an automatic system which is able to detect and quantify different biological structures of interest (such as vascular structures) which are immunohistochemically stained in different microscopic images of contiguous carotid sections containing plaques. Once detected and quantified, the marked contiguous sections are registered to allow an objective visual and comparative analysis of the spatial distribution of each marker (markers relative location). The developed system additionally computes novel measures of markers co-existence in tissue volumes depending on their density. Since each marker allows to detect a particular biological structure of interest, the accurate analysis and study of the computed densities and co-localization measures is considered by surgeons and biological scientists as a valid help to discover structures whose appearance could be exploited as an early alert of plaque instability, avoiding unnecessary surgical procedures. Discovering factors positively or negatively relate at plaques' instability would have an high impact on cerebral stroke prevention [56,12,55]. The developed system (called MIAQuant) has been lately adapted and generalized through the usage of machine learning techniques in order to be able to process images depicting tissue sections belonging to different body structures. Precisely, the novel system (called MIAQuant Learn) extracts, quantifies and analyze the co-existence of markers characterized by any color and shape and being stained in contiguous sections extracted from any body tissue [51,11,10,9].
The promising results obtained by the MIAQuant Learn motivate its extensive usage in the oncological field to quantify and analyze cancerous tissues images produced either by Ospedale San Raffaele (Milano) and by the Department of Experimental Oncology and Molecular Medicine (Fondazione IRCCS Istituto Nazionale dei Tumori).
During the year 2017 she started invenstigating the field of human color constancy and she studied several color correction algorithms based and/or derived from Land's "Retinex Theory". She is currently applying the STRESS algorithm to histological images with the aim of improving the segmentation results obtained by the aforementioned systems. At the present she is improving MIAQuant Learn to segment and count cell nuclei in images marked with ki67. A precise nuclei detection and count would allow to estimate the so-called ki67 index, a measure of tumour aggressiveness [5].
She is also investigating algorithms aimed at classifying the prognosis of patients with tumors. These classifications are computed based on the similarity between patients, and the similarity is computationally represented by graphs. The results obtained are promising [54,53,8,1].